EVALUATING THE EFFECT OF BIOMATERIALS ONTO A549 CELL USING DEEP LEARNING

Abstract

This study investigates the performance of 28 different UNet models for segmenting and determining cell confluence in brightfield microscopy images, combining various hyperparameters such as loss functions, batch sizes, and epochs. Ground truths for the images were manually annotated which was another challenge of this study. Among the models, two of them were chosen since they achieved high accuracy results. The study also evaluates the effects of different biomaterial density on cell growth using these models. The results showed that low-density biomaterials (5 ug) were non-toxic, while medium (20 ug) and high concentrations (50 ug for PAR30 and 500 ug for PLL250) significantly suppress cell growth, with confluence ratios dropping below 70%. Additionally, various classification models were tested on datasets with different cell images and biomaterial densities. Principal Component Analysis (PCA) and hybrid models were found to significantly improve classification accuracy, particularly in binary classification tasks, which achieved accuracies nearing 98%. The study highlights the performance of different model architectures, manual annotation for ground truth, and dimensionality reduction techniques in enhancing the accuracy of cell confluence segmentation and biomaterial toxicity assessment.